Forecasting with Many Predictors: How Useful are National and International Confidence Data?

2018 ◽  
Author(s):  
Kevin Moran ◽  
Nono Simplice Aime ◽  
Imad Rherrad
Keyword(s):  
Confidence Data

How cops see themselves

2016 ◽  
Vol 18 (4) ◽  
pp. 273-280 ◽  
Author(s):  
Richard Tewksbury ◽  
Allen Copenhaver
Keyword(s):  
Police Officer ◽  
Food Consumption ◽  
Police Officers ◽  
Fast Food ◽  
Police Agency ◽  
Health And Wellness ◽  
Work Shift ◽  
Fast Food Consumption ◽  
State Police ◽  
Confidence Data

This study provides the first assessment of officer self-regard and physical confidence among a sample of state police officers. Specifically, this study aims to identify health and wellness predictors of officer self-regard and physical confidence. Data were collected from surveys of all sworn members of one state police agency. State police officer self-regard is predicted by officers’ work shift, officer exercise, fast food consumption, officer sleep, and officers experiencing depression. No significant predictors of officer physical confidence were identified. This is the only study of health and wellness predictors of officer self-regard and physical confidence. Several health and wellness factors are predictive of officer self-regard, but more research should be conducted to identify health and wellness predictors of officer physical confidence.

scholarly journals Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

2020 ◽  
Author(s):  
Joshua Calder-Travis ◽  
Rafal Bogacz ◽  
Nick Yeung
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Response Times ◽  
Time Dependent ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Dynamic Stimuli ◽  
Computationally Expensive ◽  
Insight Into ◽  
Confidence Data

AbstractMuch work has explored the possibility that the drift diffusion model, a model of response times and choices, could be extended to account for confidence reports. Many methods for making predictions from such models exist, although these methods either assume that stimuli are static over the course of a trial, or are computationally expensive, making it difficult to capitalise on trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model with time-dependent thresholds, and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence which has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli which change over the course of a trial with normally distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions only contain a small number of standard functions, and only require evaluating once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.

scholarly journals AUDITING FIRMS IN THE MOROCCAN MARKET: A STUDY BASED ON CONFIDENCE, DATA FROM 2012 TO 2017.

2017 ◽  
Vol 5 (8) ◽  
pp. 1350-1356 ◽  
Author(s):  
Jubilee Hatai ◽  
◽  
Apurba Ganguly ◽  
SudipK Banerjee ◽  
Beauty Hatai ◽  
...  
Keyword(s):  
Confidence Data

scholarly journals Confidence as a noisy decision reliability estimate

2021 ◽  
Author(s):  
Zoe M Boundy-Singer ◽  
Corey M Ziemba ◽  
Robbe LT Goris
Keyword(s):  
Cognitive Tasks ◽  
Decision Making Process ◽  
Reliability Estimate ◽  
Decision Accuracy ◽  
Parsimonious Explanation ◽  
Response Biases ◽  
Computational Processes ◽  
Over Time ◽  
Confidence Data

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgments. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subject's estimate of the reliability of their decision. The quality of this estimate is limited by the subject's uncertainty about the uncertainty of the variable that informs their decision ("meta-uncertainty"). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, revealing that meta-uncertainty varies across subjects, is stable over time, generalizes across some domains, and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

Augmented Mirrors

2016 ◽  
Vol 60 (1) ◽  
pp. 1590-1594 ◽  
Author(s):  
Missie Smith ◽  
Valerie Kane ◽  
Joseph L. Gabbard ◽  
Gary Burnett ◽  
David R. Large
Keyword(s):  
Augmented Reality ◽  
Depth Perception ◽  
Driving Simulator ◽  
Distance Estimation ◽  
Vehicle Speed ◽  
Target Vehicle ◽  
Confidence Levels ◽  
Distance Data ◽  
And Performance ◽  
Confidence Data

This study investigates the effects of Augmented Reality (AR) graphics on a drivers’ distance estimation and depth perception when using a video-based, AR-enhanced driver’s side mirror. Sixteen participants took part in the study, eight in a driving simulator and eight outside in a stationary vehicle. Participants experienced three different AR display image conditions, three different glance patterns, three different target vehicle speeds, and two own-vehicle image conditions. Distance data and confidence data were collected for each particpant and analyzed for any correlation between the conditions and performance. The results suggest that various AR images affected depth judgements and confidence levels. In addition, the vehicle speed and glance pattern of the videos also had significant effects.

scholarly journals Recent Trends in Institutional Confidence Among Arkansans: Updating Earlier Findings

1986 ◽  
Vol 6 ◽  
pp. 84-92
Author(s):  
Robert L. Savage ◽  
William R. Darden
Keyword(s):  
Public Opinion ◽  
Regional Differences ◽  
The State ◽  
Public Confidence ◽  
Recent Trends ◽  
Confidence Data

In a study reporting on regional differences in public opinion among Arkansans in 1982, Savage and Blair (1984: 65) assert "that more altruistic, less remote, and more decentralized institutions receive stronger votes of confidence." Data from a 1984 survey of Arkansans do not contradict that assertion but do allow an assessment of trends in public confidence among citizens of the state during the early 1980’s as well as affording comparisons with confidence trends in the nation as a whole and in a sister state located in the industrial northeast.

scholarly journals Social Recommendation Model with User Trust and Item Ratings Using Collaborative Filtering Technique in Hotel Application

2020 ◽  
Vol 01 (01) ◽  
pp. 17-22
Author(s):  
Mary Harin Fernandez F ◽  
◽  
Ramya S ◽  
Revathy V ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Security And Privacy ◽  
Social Recommendation ◽  
Friend Recommendation ◽  
Trust Networks ◽  
Filtering Technique ◽  
Privacy Issues ◽  
User Trust ◽  
Confidence Data

A trust-based recommendation model is regularized with user trust and item ratings called TrustSVD. Trust networks are large-world networks where many users are socially linked, suggesting the assumption of trust in recommendation systems. An item rating downloaded from the OSN Server can be viewed by the user. If the information is accessible on the server, all the adjacent devices are enabled and a peer to peer mode of communication is initiated. User reviews from a graphical forum are shown. It focuses on the rating prediction role in the current framework and has shown that integrating user social confidence data will boost the output of recommendations. The strategy builds on the SVD++ state-of-the-art model. The data sparsity and cold start issues are resolved in the friend of friend recommendation model used. The mining method generates the user's overall rating in graphical representations and illustrates the overall rating. This model increases the utility of data by exchanging neighborhoods to protect security and privacy issues. One of the most common techniques for implementing a recommendation scheme is Collaborative filtering (CF).

scholarly journals Hubungan Self Concept dan Self Confidence

2020 ◽  
Vol 7 (1) ◽  
pp. 41-55
Author(s):  
Sholiha Sholiha ◽  
Lailatuzzahro Al-Akhda Aulia
Keyword(s):  
Data Analysis ◽  
Random Sampling ◽  
Sampling Technique ◽  
Quantitative Approach ◽  
Self Concept ◽  
Stratified Random Sampling ◽  
Self Confidence ◽  
The Subject ◽  
The Individual ◽  
Confidence Data

A confidence and an attitude toward a persons ability to accept both positive and negative that are shaped and learned through the process of studying with a view to his own happiness. While self concept is an idea, mind, feeling, belief and position known by the individual in relating to others. The purpose of this study is to know a self concept relationship with a self confident. The hypothesis of this study is there's a self concept relationship with a self confidence. In the study, researchers used a quantitative approach in an attempt to test hypotheses that had been drafted. The subject in this study is a teenager sitting in class 7 and 8 MTS miftahul ulum pandaan. The sampling technique used was staggerate stratified random sampling. The analysis of data in this study uses the scale of self concept and self confidence. Data analysis was calculated by using product moment Pearson and acquired rxy by 0.657 with an r-tab 1% of 0.254. Thus there is a highly significant correlation between self concept and a self confidence.

AUDITING FIRMS IN THE MOROCCAN MARKET: A STUDY BASED ON CONFIDENCE, DATA FROM 2012 TO 2017.

2017 ◽  
Vol 5 (8) ◽  
pp. 1330-1343 ◽  
Author(s):  
CHEGRIBadre Eddine ◽  
◽  
ELMENZHI Kaoutar ◽  
AITKASSI Mustapha. ◽  
◽  
...  
Keyword(s):  
Confidence Data

CONFIDENCE, DATA PROTECTION AND THE SUPERMODEL

2003 ◽  
Vol 62 (1) ◽  
pp. 17-20
Author(s):  
David Howarth
Keyword(s):  
Data Protection ◽  
Confidence Data
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